Beyond Technology by Drs. Stanley Liu and Nasim Mesgarzadeh

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Beyond Technology 

AI-augmented clinical reasoning in dental medicine

by Drs. Stanley Y.C. Liu and Nasim Mesgarzadeh

Dentistry is entering a period in which artificial intelligence, automation, and robotics are steadily integrating into everyday professional activity. These systems now appear in imaging workflows, clinical documentation, patient communication, and laboratory manufacturing pipelines. The central consideration for clinicians is how these capabilities influence the quality of clinical reasoning and patient outcomes as they become part of routine practice.

Health care environments are inherently sensitive to rapid technological adoption, as new tools often enter practice accompanied by persuasive demonstrations and promises of efficiency. Sustained clinical excellence, however, arises from disciplined judgment, rigorous biological understanding, and decisions made under uncertainty. A durable path to innovation begins with deep fluency in the disease process itself, particularly in longitudinal and multifactorial conditions where mechanisms, progression patterns, and patient-specific variability unfold over time. Technology can broaden reach and sharpen measurement, yet the intellectual and ethical foundations of care remain anchored in human interpretation of physiology, pathology, and evidence.

Meaningful progress in dental medicine is driven by focusing effort on the questions that materially influence outcomes and long-term health rather than by the introduction of new tools alone. Directing attention toward the variables that truly move the clinical needle reveals where technology serves as a constructive ally and where it merely adds noise. When inquiry is precise and oriented toward high-impact problems, digital systems become tools for synthesis and organization, supporting clinicians in navigating complexity without displacing responsibility.

Ultimately the value of this level of technological integration becomes visible through measurable improvements in patient outcomes, treatment precision, and long-term health outcomes.


A needs-first lens: The biodesign approach applied to dental practice
One of the most practical innovation frameworks in health care originates from the Biodesign tradition, commonly summarized in three words: identify, invent, and implement.1

Identify (Needs discovery)
Innovation begins with disciplined observation and comprehensive understanding of unmet needs before solutions are considered. This phase includes mapping decision breakdowns, defining problems in outcome-based language, and validating needs through both direct clinical observation and existing evidence. In Biodesign terminology, a precisely articulated need serves as the genetic code of a strong solution. Clarity at this stage shapes every subsequent step.

Invent (Solution exploration)
Once the need is clearly articulated, multiple pathways are explored, including technical, procedural, educational, and workflow-based options. Each pathway is evaluated against feasibility, clinical significance, and potential unintended consequences. Effective exploration requires maintaining alignment with the original need statement and preserving measurable clinical relevance throughout development.

Implement (Real-world translation)
Implementation planning begins early and evolves continuously. This phase evaluates workflow integration, interdisciplinary coordination, privacy and data stewardship, liability, bias, regulatory considerations, training demands, and cost alongside clearly defined outcome metrics. Many promising innovations achieve lasting impact only when they integrate seamlessly into clinical reality and organizations.

This needs-first mindset holds particular relevance for dentistry, where decision-making can be longitudinal and multifactorial. Records are distributed across imaging, charting, laboratory outputs, and patient behaviors, while time limitations and heterogeneous risk profiles remain constant. Systems that consolidate information and support synthesis often produce meaningful gains in clarity and consistency.1,2


What changed in AI: Why transformers matter
Artificial intelligence is frequently encountered in practice as a set of discrete products such as imaging enhancements, documentation assistants, or scheduling tools. The recent shift, however, reflects a structural advance in how machines represent relationships within data.

The introduction of the Transformer architecture in 2017 enabled models to evaluate relationships among all elements of an input simultaneously through attention mechanisms rather than sequential processing.3 This architectural evolution expanded the practical capacity of machines to manage large context windows, synthesize extended documents, reconcile conflicting information, and generate structured summaries. For clinicians, the significance lies in expanded analytical capability rather than computational detail.

Subsequent progress in large language models introduced complementary strengths. Broad language understanding emerged through large-scale unsupervised pre-training, while increased scale and data exposure enabled performance across multiple tasks through natural-language instruction alone. These developments established systems capable of drafting, organizing, and integrating complex information across extended contexts.4,5 In dental medicine, these capabilities support continuity and depth of clinical reasoning. In practical settings, modern artificial intelligence functions as an organizational and analytical assistant.


How AI models become helpful: pre-training, fine tuning, and feedback
Modern language models do not appear fully formed as helpful assistants; they are built through a multi-stage training pipeline. During pre-training, a neural network ingests billions of tokens scraped from books, articles, and websites to learn how to predict the next word. This produces a powerful base model that can autocomplete text but does not yet understand human intent. Companies then perform supervised fine-tuning (SFT) using curated question–answer pairs. Human experts craft ideal prompt–response examples that teach the model to behave like a friendly assistant and to organize information coherently. Finally, reinforcement learning further refines the model by rewarding responses that users prefer. In reinforcement learning from human feedback (RLHF), human evaluators rank multiple candidate outputs for the same prompt; a separate reward model learns these rankings, and the base model is adjusted to maximize the reward. Through RLHF, a raw simulator becomes a model that follows instructions, reduces hallucinations, and stays aligned with human values.6 These alignment techniques are still evolving and increasingly include multimodal capabilities such as image and video processing; therefore, clinicians should remain aware that even RLHF-trained models require ongoing oversight.


Concrete tools for academic dentistry
Academic professionals in dentistry derive the greatest benefit from technologies that reduce friction in high-stakes domains such as writing and evidence navigation.

Prism represents a cloud-based, LaTeX-native writing environment in which drafting, revision, collaboration, and publication preparation occur within a unified structure. The defining advantage is structural integration.7 The model operates within the architecture of the document, accessing sections, references, and equations as part of a continuous workflow rather than functioning as a detached assistant.

PaperQA illustrates a retrieval-augmented approach to scientific literature navigation.8 Instead of relying solely on internal model memory, it retrieves relevant full-text passages, evaluates relevance, and composes responses with explicit provenance. For dentistry, this method enhances auditability and supports disciplined evidence synthesis across heterogeneous study designs and patient populations while preserving clinician oversight.


Making AI useful while preserving responsibility
A needs-first approach clarifies both opportunity and risk. Recognized concerns include privacy vulnerabilities, biased outputs, excessive reliance on automation, and insufficient documentation of clinician reasoning. Practical professional standards maintain balance and accountability: Artificial intelligence outputs function best as drafts or decision aids. Patient data handling requires continuous compliance with ethical and legal standards. Human review remains essential for high-stakes communication and irreversible clinical decisions. Measured evaluation of outcomes informs ongoing refinement. Explicit team protocols establish appropriate boundaries for use and documentation. Continuous clinician education ensures tools are used with competence rather than convenience.


Conclusion: The discipline of asking better questions
The next era of dental medicine will be shaped by disciplined integration of technology into clinical reasoning grounded in biological understanding, patient outcomes, and ethical responsibility. The Biodesign framework offers a durable template: Identify authentic unmet needs, explore multiple solution pathways, and implement with rigor and measurement. The emergence of Transformer architectures in 2017, followed by large-scale language models in the early 2020s, did not represent a gradual enhancement but rather a structural inflection point in computational capability. Transformer-based artificial intelligence marks a pivotal shift not merely in processing power, but in how information is perceived—enabling machines to evaluate relationships across entire datasets simultaneously rather than sequentially, revealing patterns and contextual links that might otherwise remain fragmented. In this sense, artificial intelligence functions not only as a tool, but as a shift in the architecture of knowledge that increasingly shapes how professionals interact with information itself.

When paired with evidence-based and workflow-integrated systems, this relational perspective becomes an analytical infrastructure that augments, rather than replaces, clinician judgment. The enduring objective remains clinical wisdom informed by technology, expressed through sharper questions, clearer pattern recognition, deliberate decision-making, and an improved ability to recognize questions that might otherwise go unasked.

References
1. Zenios S, Makower J, Yock P, Brinton TJ, Kumar UN, Denend L. Biodesign: The Process of Innovating Medical Technologies. 2nd ed. Cambridge University Press; 2015.
2. Liu SYC, Awad M, Riley R, Capasso R. The role of the revised Stanford protocol in today’s precision medicine. Sleep Med Clin. 2019;14(1):99–107.
3. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Adv Neural Inf Process Syst. 2017;30:5998–6008.
4. Radford A, Narasimhan K, Salimans T, Sutskever I. Improving language understanding by generative pre-training. OpenAI; 2018.
5. Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I. Language models are unsupervised multitask learners. OpenAI; 2019.
6. Ouyang L, Wu J, Jiang X, et al. Training language models to follow instructions with human feedback. OpenAI; 2022.
7. OpenAI. Prism Writing Workspace. Accessed February 2026. https://openai.com/prism
8. Lewis P, Perez E, Piktus A, et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. Adv Neural Inf Process Syst. 2020.

Author Bio
Dr. Stanley Y.C. Liu Stanley Y. C. Liu, MD, DDS, FACS, is a dual-trained oral and maxillofacial surgeon and ENT sleep surgeon dedicated to the diagnosis and treatment of sleep-breathing disorders. He serves as chair of oral and maxillofacial surgery at Nova Southeastern University and director of the NSU Health Breathe & Sleep Wellness Center, where his work advances airway-focused surgical innovation and interdisciplinary care. He has led the sleep surgery fellowship within Stanford Otolaryngology, helping shape the next generation of sleep surgeons for a decade. As a Stanford Biodesign Faculty Fellow, his guiding philosophy reflects a simple yet transformative premise: Restoring airway health is a gateway to wellness.
Dr. Nasim Mesgarzadeh Nasim Mesgarzadeh, DDS, PhD, MBA, is an orthodontist with post-residency fellowship training in craniofacial, surgical, and special-care orthodontics. Her work integrates surgical-related orthodontic care with emerging AI technologies, emphasizing interdisciplinary collaboration with maxillofacial, plastic, and sleep surgery, and advancing intelligence-augmented orthodontic practice. She is a member of the OpenAI Forum, engaging at the intersection of clinical medicine and emerging artificial intelligence.
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April 2026
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